This invention relates to active control of acoustic and structural vibrations with an uncertain vibration statistic or propagation path, which control is achieved by attenuating at least one primary (i.e. unwanted) vibration, at least in part, by injecting at least one specially generated cancelling (i.e. secondary) vibration. An acoustic vibration is gas-, liquid- or solid-borne. A structural vibration is usually a solid-borne longitudinal, torsional or flexural vibration; and is also called a mechanical vibration. Active control of acoustic and structural vibrations considered herein emcompasses active noise control, active sound control, active control of vibration, active noise cancellation, active vibration attenuation, active structural acoustic control, active acoustic and structural vibration control, etc. Active control of acoustic and structural vibrations is herein referred to as active vibration control (AVC) or active acoustic and structural vibration control (AASVC).
There is an enormous amount of research results on AVC (or AASVC). Most of these prior-art results and their applications can be found or tracked down from the books by P. A. Nelson and S. J. Elliott, Active Control of Sound, Academic Presss (1992); by S. M. Kuo and D. R. Morgan, Active Noise Control Systemsxe2x80x94Algorithms and DSP Implementations, John Wiley and Sons, Inc. (1996); by C. R. Fuller, Active Control of Vibration, Academic Presss (1996); and by C. H. Hansen and S. D. Snyder, Active Control of Noise and Vibration, E and F N Spon (1997), and from the articles and journals referred to in these books. To facilitate discussion on the background of the invention, four basic AVC (i.e. AASVC) systems in the prior art for actively controlling acoustic and structural vibrations with an uncertain vibration statistic or propagation path are briefly described and their shortcomings discussed here.
For brevity, signal conditioners for converting the outputs of sensors and controllers into proper signal forms (e.g. digital signals within a certain frequency band and amplitude range) suitable for subsequent processings are omitted in the discussions in this section on the background of the present invention. An example signal conditioner, that for conditioning the output of an acoustic sensor, comprises a preamplifier, an anti-aliasing filter and an A/D converter.
A first basic AVC system, which is usually used for attenuating a broadband primary vibration, comprises a vibrational reference sensor (e.g. a microphone, hydrophone, accelerometer, velocity transducer, displacement transducer, or strain sensor), a secondary source (e.g. a loudspeaker, horn, underwater sound projector, piezoeletric actuator, electromagnetic shaker, hydraulic actuator, pneumatic actuator, proof mass actuator, electrodynamic and electromagnetic actuator, magnetostrictive actuator, or shape memory alloy actuator), an error sensor (e.g. a microphone, hydrophone, accelerometer, velocity transducer, displacement transducer, or strain sensor) and an adaptive linear filter used as a controller to drive the seconary source. The adaptive linear filter inputs the reference signal from the reference sensor and outputs a control signal for driving the seconary source. Using the error signal from the error sensor and a (mathematical) model of the secondary path (from the output of the adaptive linear filter to the output of the error sensor), an FXLMS (i.e. the filtered-x least-mean-square) algorithm is used to adjust the weights of the adaptive linear filter online to reduce the error signal from the error sensor.
A second basic AVC system, which is usually used for attenuating narrowband primary vibration whose waveform is ordinarily periodic or nearly periodic between its changes, comprises a nonvibrational reference sensor (e.g. a magnetic or optical pickup sensing the rotation of a toothed wheel), a seconary source, an error sensor and a waveform synthesizer. The nonvibrational reference sensor provides a synchronization signal for the waveform synthesizer to lock on to. Acting as a controller, the waveform synthesizer inputs the nonvibrational reference signal from the nonvibrational reference sensor and outputs a synthesized waveform for driving the seconary source. Using the error signal from the error sensor, an adaptation unit is used to adjust the synthesized waveform online to reduce the error signal from the error sensor.
A third basic AVC system uses no (vibrational or nonvibrational) reference sensor and is usually used for attenuating colored (e.g. correlated) primary vibration. It comprises a secondary source, an error sensor and an adaptive linear filter used as a controller. An estimate of the primary vibration component of the error signal from the error sensor is obtained by adding the error signal and an estimate of the secondary vibration component of the error signal, which is obtained by passing the control signal from the adaptive linear filter through a model of the secondary path. The adaptive linear filter inputs said estimate of the primary vibration component and outputs a control signal for driving the secondary source. Using the error signal from the error sensor and the model of the secondary path, an FXLMS algorithm is used to adjust the weights of the adaptive linear filter online to reduce the error signal.
A fourth basic AVC system, which is usually used for attenuating primary vibration of a large mechanical system or inside an enclosure or a large-dimension duct, comprises multiple reference sensors, multiple output actuators, multiple error sensors and an adaptive linear filter as a controller. The adaptive linear filter inputs the reference signals from the reference sensors and outputs a control signal for driving each of the output actuators. Using the error signal from each of the error sensors and the models of the secondary paths (from the output of the adaptive linear filter to each output of error sensors), a multiple-channel FXLMS (filtered-x least-mean-square) algorithm is used to adjust the weights of the adaptive linear filter online to reduce the error signals.
These basic and other AVC systems in the prior art for actively controlling acoustic and structural vibrations with an uncertain vibration statistic or propagation path suffer from at least one of the following shortcomings:
1. Use of an error sensor at each objective point: If a primary path (from the output of a reference sensor to the output of an error sensor) or a secondary path is uncertain (i.e. unknown or time-varying) and if either the weights of an adaptive linear filter or a synthesized waveform generated by a waveform synthesizer is adjusted online, one error sensor must be used at each objective point, which adds to the cost of the AVC system and may cause inconvenience. In some applications, an error sensor senses not only primary and secondary vibrations but also a third vibration that is correlated with the primary vibration and thus degrades the AVC performance.
2. Relatively slow convergence of a weight/waveform adjustment algorithm: If a primary path or a secondary path undergoes a relatively rapid change, an LMS algorithm (e.g. an FXLMS algorithm or a filtered-u recursive LMS algorithm), a RLS algorithm or an adaptation unit is not fast enough in adjusting the weights of an adaptive linear filter or the synthesized waveform generated by a waveform synthesizer used as a controller for some applications.
3. Online or frequent modelling of a secondary path: If a secondary path is time-varying, a model of it needs to be adjusted either online or offline from time to time.
4. Use of a high-order adaptive linear transversal filter: If an adaptive linear recursive filter is the natural one to be used as a controller or as a model of a secondary path, but an adaptive linear transversal filter is used instead to approximate it, the order of the adaptive linear transversal filter is usually large and requires much computation for its weight adjustment. In fact, the better the approximation is wanted, the larger the order is required.
5. Instability of an adaptive linear recursive filter: If an adaptive linear recursive filter is used as a controller or as a model of a secondary path, its poles may lie outside of the unit circle during the weight adjustment process, causing instability. Besides, the performance surface of an adaptive linear recursive filter is generally nonconvex and thus has poor local extrema. Furthermore, an adaptive linear recursive filter has a slower convergence rate than an adaptive linear recursive filter.
6. Failure to use a useful nonvibrational reference sensor: A waveform synthesizer usually uses the synchronization information from a nonvibrational reference sensor. Many other forms of information about the primary vibration waveform that can be provided by various different nonvibrational sensors are not effectively used. For example, the electric current drawn by a electric fan or motor; the electric current generated by a power generater; the road surface condition for an automobile; the position of the throttle valve, the intake manifold vacuum and mass air flow in an internal combustion engine, etc.
7. Failure to deal with nonlinearity in a primary or secondary path: If a primary or secondary path has nonlinear behavior, there is room for improvement in the performance of an adaptive linear filter used as a model of a secondary path or as a controller to drive a secondary source.
In U.S. Pat. No. 5,434,783 issued in 1995 to Chinmoy Pal and Kamakura Hagiwara, AVC systems for actively reducing acoustic vibrations in a vehicular compartment and/or vehicular body vibrations, that have uncertain statistics and propagation paths, by the use of neural networks are disclosed. They suffer from the following shortcomings:
1. Use of an error sensor at each objective point: Error sensors add to the cost of the AVC system. In the case of reducing acoustic vibration in a vehicular compartment, some objective points may have to be chosen either at places causing inconvenience to the passengers in the vehicular compartment or at places causing lower AVC performance. Moreover, the error sensors sense not only the primary and secondary vibrations but also the music and/or conversation inside the vehicular compartment, which degrades the AVC performance.
2. Weight adjustment using control predicted values: The weights (e.g. parameters) of a control neural network in a control unit are adjusted by comparing a control predicted value with a control target value. The control predicted value is only an approximate of the the true error signal. Since this approximation error is included in adjusting the weights of the control neural network, the performance of the control neural network is degraded.
3. Use of an identification (i.e. modeling) neural network: If an identification neural network is used to generate a control predicted value, it takes a large amount of additional computation to run and to adapt this identification neural network.
4. Online adjustment of the weights of a neural network: Online weight adjustment for a neural network takes much computation, may fall into a poor local minimum of the performance surface of the control neural network, and needs to wait a large number of time steps to collect sufficient amount of information for determining the weights.
AVC systems for actively controlling acoustic and structural vibrations with an uncertain statistic or propagation path, that use neural networks, are also reported in the journal articles by S. D. Snyder and N. Tanaka, xe2x80x9cActive control of vibration using a neural network,xe2x80x9d IEEE Transactions on Neural Networks, Vol. 6, No. 4, 1995; and by M. Bouchard, B. Paillard, and C. T. L. Dinh, xe2x80x9cImproved training of neural networks for the nonlinear active control of sound and vibration,xe2x80x9d IEEE Transactions on Neural Networks, Vol. 10, No. 2, 1999. In these articles, two multilayer perceptrons (or feedforward networks) are used respectively as the controller and the secondary path model. No recursive neural network is mentioned. However, the AVC systems therein also suffer from the shortcomings of the use of error sensors, the use of an identification neural network and the online adjustment of the weights of a neural network as stated above for the AVC systems desclosed in U.S. Pat. No. 5,434,783.
Because of the foregoing shortcomings of the AVC systems for actively controlling acoustic and structural vibrations with an uncertain statistic or propagation path from the books by P. A. Nelson and S. J. Elliott, Active Control of Sound, Academic Presss (1992) and by S. M. Kuo and D. R. Morgan, Active Noise Control Systemsxe2x80x94Algorithms and DSP Implementations, John Wiley and Sons, Inc. (1996); the AVC systems from U.S. Pat. No. 5,434,783 issued to Chinmoy Pal and Kamakura Hagiwara (1995); and the AVC systems from the journal articles by S. D. Snyder and N. Tanaka, xe2x80x9cActive control of vibration using a neural network,xe2x80x9d IEEE Transactions on Neural Networks, Vol. 6, No. 4, 1995; and by M. Bouchard, B. Paillard, and C. T. L. Dinh, xe2x80x9cImproved training of neural networks for the nonlinear active control of sound and vibration,xe2x80x9d IEEE Transactions on Neural Networks, Vol. 10, No. 2, 1999, there is a need for better AVC (i.e. AASVC) systems for actively controlling acoustic and structural vibrations with an uncertain statistic or propagation path.
It is an object of the present invention to provide an active vibration control (AVC) system for actively controlling acoustic or/and structural vibration(s) with an uncertain statistic or propagation path that requires neither online controller adjustment nor online propagation path modeling and thereby reduces or eliminates some or all of the shortcomings of the prior-art AVC systems. As described in the preceding BACKGROUND section, these shortcomings include use of an error sensor at each objective point, relatively slow convergence of a weight/waveform adjustment algorithm, online or frequent adjustment of a model of a secondary path, use of a high-order adaptive linear transversal filter, instability of an adaptive linear recursive filter, failure to use a useful nonvibrational reference sensor, failure to deal with the nonlinear behavior of a primary or secondary path, weight adjustment using control predicted values, use of an identification neural network, and online adjustment of the weights of a neural network.
The above-stated object can be achieved by providing an AVC (feedforward) system comprising reference sensor means for deriving at least one reference signal containing information about at least one primary vibration; a recursive neural network, comprising a plurality of weights, for processing said at least one reference signal to generate at least one control signal; and secondary source means, comprising at least one secondary source, for converting said at least one control signal into at least one cancelling vibration, wherein said weights are held fixed online during the operation of said AVC feedforward system.
The above-stated object can also be achieved by providing an AVC (feedforward-feedback) system comprising reference sensor means for deriving at least one reference signal containing information about at least one primary vibration; error sensor means for sensing a combination of at least one primary vibration and at least one cancelling vibration to provide at least one error signal; a recursive neural network, comprising a plurality of weights, for processing said at least one reference signal and said at least one error signal to generate at least one control signal; and secondary source means, comprising at least one secondary source, for converting said at least one control signal into said at least one cancelling vibration, wherein said weights are held fixed online during the operation of said feedforward-feedback AVC system.
The above-stated object can also be achieved by providing an AVC (feedback) system comprising error sensor means for sensing a combination of at least one primary vibration and at least one cancelling vibration to provide at least one error signal; a recursive neural network, comprising at least one nonlinear hidden neuron and a plurality of weights, for processing said at least one error signal to generate at least one control signal; and secondary source means, comprising at least one secondary source, for converting said at least one control signal into said at least one cancelling vibration, wherein said weights are held fixed online during the operation of said AVC feedback system.
In every AVC system disclosed herein for actively controlling acoustic and structural vibrations with an uncertain statistic or propagation path, there is no online modeling of a primary, feedback or secondary path, and all weights of the recursive neural network used as a controller are held fixed online during the operation of the AVC system. The recursive neural network, with proper offline a priori training, is able to adapt to every uncertain vibration statistic or propagation path of the AVC system.